/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    OrdinalClassClassifier.java
 *    Copyright (C) 2001 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.meta;

import weka.classifiers.Classifier;
import weka.classifiers.SingleClassifierEnhancer;
import weka.classifiers.rules.ZeroR;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.MakeIndicator;

import java.util.Enumeration;
import java.util.Vector;

/**
 * <!-- globalinfo-start --> Meta classifier that allows standard classification
 * algorithms to be applied to ordinal class problems.<br/>
 * <br/>
 * For more information see: <br/>
 * <br/>
 * Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th
 * European Conference on Machine Learning, 145-156, 2001.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- technical-bibtex-start --> BibTeX:
 * 
 * <pre>
 * &#64;inproceedings{Frank2001,
 *    author = {Eibe Frank and Mark Hall},
 *    booktitle = {12th European Conference on Machine Learning},
 *    pages = {145-156},
 *    publisher = {Springer},
 *    title = {A Simple Approach to Ordinal Classification},
 *    year = {2001}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <pre>
 * -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.trees.J48)
 * </pre>
 * 
 * <pre>
 * Options specific to classifier weka.classifiers.trees.J48:
 * </pre>
 * 
 * <pre>
 * -U
 *  Use unpruned tree.
 * </pre>
 * 
 * <pre>
 * -C &lt;pruning confidence&gt;
 *  Set confidence threshold for pruning.
 *  (default 0.25)
 * </pre>
 * 
 * <pre>
 * -M &lt;minimum number of instances&gt;
 *  Set minimum number of instances per leaf.
 *  (default 2)
 * </pre>
 * 
 * <pre>
 * -R
 *  Use reduced error pruning.
 * </pre>
 * 
 * <pre>
 * -N &lt;number of folds&gt;
 *  Set number of folds for reduced error
 *  pruning. One fold is used as pruning set.
 *  (default 3)
 * </pre>
 * 
 * <pre>
 * -B
 *  Use binary splits only.
 * </pre>
 * 
 * <pre>
 * -S
 *  Don't perform subtree raising.
 * </pre>
 * 
 * <pre>
 * -L
 *  Do not clean up after the tree has been built.
 * </pre>
 * 
 * <pre>
 * -A
 *  Laplace smoothing for predicted probabilities.
 * </pre>
 * 
 * <pre>
 * -Q &lt;seed&gt;
 *  Seed for random data shuffling (default 1).
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author <a href="mailto:mhall@cs.waikato.ac.nz">Mark Hall</a>
 * @version $Revision 1.0 $
 * @see OptionHandler
 */
public class OrdinalClassClassifier extends SingleClassifierEnhancer implements
		OptionHandler, TechnicalInformationHandler {

	/** for serialization */
	static final long serialVersionUID = -3461971774059603636L;

	/** The classifiers. (One for each class.) */
	private Classifier[] m_Classifiers;

	/** The filters used to transform the class. */
	private MakeIndicator[] m_ClassFilters;

	/** ZeroR classifier for when all base classifier return zero probability. */
	private ZeroR m_ZeroR;

	/**
	 * String describing default classifier.
	 * 
	 * @return the default classifier classname
	 */
	protected String defaultClassifierString() {

		return "weka.classifiers.trees.J48";
	}

	/**
	 * Default constructor.
	 */
	public OrdinalClassClassifier() {
		m_Classifier = new weka.classifiers.trees.J48();
	}

	/**
	 * Returns a string describing this attribute evaluator
	 * 
	 * @return a description of the evaluator suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String globalInfo() {
		return "Meta classifier that allows standard classification algorithms "
				+ "to be applied to ordinal class problems.\n\n"
				+ "For more information see: \n\n"
				+ getTechnicalInformation().toString();
	}

	/**
	 * Returns an instance of a TechnicalInformation object, containing detailed
	 * information about the technical background of this class, e.g., paper
	 * reference or book this class is based on.
	 * 
	 * @return the technical information about this class
	 */
	public TechnicalInformation getTechnicalInformation() {
		TechnicalInformation result;

		result = new TechnicalInformation(Type.INPROCEEDINGS);
		result.setValue(Field.AUTHOR, "Eibe Frank and Mark Hall");
		result.setValue(Field.TITLE,
				"A Simple Approach to Ordinal Classification");
		result.setValue(Field.BOOKTITLE,
				"12th European Conference on Machine Learning");
		result.setValue(Field.YEAR, "2001");
		result.setValue(Field.PAGES, "145-156");
		result.setValue(Field.PUBLISHER, "Springer");

		return result;
	}

	/**
	 * Returns default capabilities of the classifier.
	 * 
	 * @return the capabilities of this classifier
	 */
	public Capabilities getCapabilities() {
		Capabilities result = super.getCapabilities();

		// class
		result.disableAllClasses();
		result.disableAllClassDependencies();
		result.enable(Capability.NOMINAL_CLASS);

		return result;
	}

	/**
	 * Builds the classifiers.
	 * 
	 * @param insts
	 *            the training data.
	 * @throws Exception
	 *             if a classifier can't be built
	 */
	public void buildClassifier(Instances insts) throws Exception {

		Instances newInsts;

		// can classifier handle the data?
		getCapabilities().testWithFail(insts);

		// remove instances with missing class
		insts = new Instances(insts);
		insts.deleteWithMissingClass();

		if (m_Classifier == null) {
			throw new Exception("No base classifier has been set!");
		}
		m_ZeroR = new ZeroR();
		m_ZeroR.buildClassifier(insts);

		int numClassifiers = insts.numClasses() - 1;

		numClassifiers = (numClassifiers == 0) ? 1 : numClassifiers;

		if (numClassifiers == 1) {
			m_Classifiers = Classifier.makeCopies(m_Classifier, 1);
			m_Classifiers[0].buildClassifier(insts);
		} else {
			m_Classifiers = Classifier.makeCopies(m_Classifier, numClassifiers);
			m_ClassFilters = new MakeIndicator[numClassifiers];

			for (int i = 0; i < m_Classifiers.length; i++) {
				m_ClassFilters[i] = new MakeIndicator();
				m_ClassFilters[i].setAttributeIndex(""
						+ (insts.classIndex() + 1));
				m_ClassFilters[i].setValueIndices("" + (i + 2) + "-last");
				m_ClassFilters[i].setNumeric(false);
				m_ClassFilters[i].setInputFormat(insts);
				newInsts = Filter.useFilter(insts, m_ClassFilters[i]);
				m_Classifiers[i].buildClassifier(newInsts);
			}
		}
	}

	/**
	 * Returns the distribution for an instance.
	 * 
	 * @param inst
	 *            the instance to compute the distribution for
	 * @return the class distribution for the given instance
	 * @throws Exception
	 *             if the distribution can't be computed successfully
	 */
	public double[] distributionForInstance(Instance inst) throws Exception {

		if (m_Classifiers.length == 1) {
			return m_Classifiers[0].distributionForInstance(inst);
		}

		double[] probs = new double[inst.numClasses()];

		double[][] distributions = new double[m_ClassFilters.length][0];
		for (int i = 0; i < m_ClassFilters.length; i++) {
			m_ClassFilters[i].input(inst);
			m_ClassFilters[i].batchFinished();

			distributions[i] = m_Classifiers[i]
					.distributionForInstance(m_ClassFilters[i].output());

		}

		for (int i = 0; i < inst.numClasses(); i++) {
			if (i == 0) {
				probs[i] = distributions[0][0];
			} else if (i == inst.numClasses() - 1) {
				probs[i] = distributions[i - 1][1];
			} else {
				probs[i] = distributions[i - 1][1] - distributions[i][1];
				if (!(probs[i] > 0)) {
					System.err.println("Warning: estimated probability "
							+ probs[i] + ". Rounding to 0.");
					probs[i] = 0;
				}
			}
		}

		if (Utils.gr(Utils.sum(probs), 0)) {
			Utils.normalize(probs);
			return probs;
		} else {
			return m_ZeroR.distributionForInstance(inst);
		}
	}

	/**
	 * Returns an enumeration describing the available options.
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {

		Vector vec = new Vector();

		Enumeration enu = super.listOptions();
		while (enu.hasMoreElements()) {
			vec.addElement(enu.nextElement());
		}
		return vec.elements();
	}

	/**
	 * Parses a given list of options.
	 * <p/>
	 * 
	 * <!-- options-start --> Valid options are:
	 * <p/>
	 * 
	 * <pre>
	 * -D
	 *  If set, classifier is run in debug mode and
	 *  may output additional info to the console
	 * </pre>
	 * 
	 * <pre>
	 * -W
	 *  Full name of base classifier.
	 *  (default: weka.classifiers.trees.J48)
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to classifier weka.classifiers.trees.J48:
	 * </pre>
	 * 
	 * <pre>
	 * -U
	 *  Use unpruned tree.
	 * </pre>
	 * 
	 * <pre>
	 * -C &lt;pruning confidence&gt;
	 *  Set confidence threshold for pruning.
	 *  (default 0.25)
	 * </pre>
	 * 
	 * <pre>
	 * -M &lt;minimum number of instances&gt;
	 *  Set minimum number of instances per leaf.
	 *  (default 2)
	 * </pre>
	 * 
	 * <pre>
	 * -R
	 *  Use reduced error pruning.
	 * </pre>
	 * 
	 * <pre>
	 * -N &lt;number of folds&gt;
	 *  Set number of folds for reduced error
	 *  pruning. One fold is used as pruning set.
	 *  (default 3)
	 * </pre>
	 * 
	 * <pre>
	 * -B
	 *  Use binary splits only.
	 * </pre>
	 * 
	 * <pre>
	 * -S
	 *  Don't perform subtree raising.
	 * </pre>
	 * 
	 * <pre>
	 * -L
	 *  Do not clean up after the tree has been built.
	 * </pre>
	 * 
	 * <pre>
	 * -A
	 *  Laplace smoothing for predicted probabilities.
	 * </pre>
	 * 
	 * <pre>
	 * -Q &lt;seed&gt;
	 *  Seed for random data shuffling (default 1).
	 * </pre>
	 * 
	 * <!-- options-end -->
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @throws Exception
	 *             if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {

		super.setOptions(options);
	}

	/**
	 * Gets the current settings of the Classifier.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {

		return super.getOptions();
	}

	/**
	 * Prints the classifiers.
	 * 
	 * @return a string representation of this classifier
	 */
	public String toString() {

		if (m_Classifiers == null) {
			return "OrdinalClassClassifier: No model built yet.";
		}
		StringBuffer text = new StringBuffer();
		text.append("OrdinalClassClassifier\n\n");
		for (int i = 0; i < m_Classifiers.length; i++) {
			text.append("Classifier ").append(i + 1);
			if (m_Classifiers[i] != null) {
				if ((m_ClassFilters != null) && (m_ClassFilters[i] != null)) {
					text.append(", using indicator values: ");
					text.append(m_ClassFilters[i].getValueRange());
				}
				text.append('\n');
				text.append(m_Classifiers[i].toString() + "\n");
			} else {
				text.append(" Skipped (no training examples)\n");
			}
		}

		return text.toString();
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 1.18 $");
	}

	/**
	 * Main method for testing this class.
	 * 
	 * @param argv
	 *            the options
	 */
	public static void main(String[] argv) {
		runClassifier(new OrdinalClassClassifier(), argv);
	}
}
